Pulling in data
## pull in jw hourly met
jw_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/joewright_met_hr_20220227.dat",
sep = ",", header=TRUE, skip="1") %>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'jw')
## pull in tc hourly met
tc_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/tunnelcreek_met2_hr_20220227.dat",
sep = ",", header=TRUE, skip="1")%>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'tc')
## pull in mc hourly met
mc_hr <- read.csv("C:/Users/sears/Documents/Research/CPF/Data_downloads/mtncampus_20220223.csv") %>%
mutate(TIMESTAMP = ymd_hm(TIMESTAMP)) %>%
mutate(hour = hour(TIMESTAMP),
yday = yday(TIMESTAMP)) %>%
group_by(yday, hour) %>%
summarize_all(list(mean)) %>%
mutate(TIMESTAMP = floor_date(TIMESTAMP, "hour")) %>%
mutate(site = 'mc')
Binding data together
## compare incoming SW between sites, put in 1 df
mc_tc <- bind_rows(mc_hr, tc_hr)
#bind all 3, then keep only rad data, filter to start on Nov1
mc_tc_jw <- bind_rows(mc_tc, jw_hr) %>%
select(c(TIMESTAMP, SWin_Avg, SWout_Avg,
LWin_Avg, LWout_Avg,
SWalbedo_Avg, site)) %>%
filter(TIMESTAMP > ymd_hms("2021-11-01 00:00:00"))
## Adding missing grouping variables: `yday`
## plot SW in 1 plot
swin <- ggplot(mc_tc_jw, aes(x = TIMESTAMP, y= SWin_Avg, color = site)) +
geom_line(size=1)
ggplotly(swin)